FlowSOM

FlowSOM visualizes and interprets cytometry data using Self-Organizing Map clustering and Minimal Spanning Trees.

Key Features:

• Self-Organizing Map Clustering: FlowSOM applies Self-Organizing Map clustering to cytometry data.
• Minimal Spanning Trees: FlowSOM uses Minimal Spanning Trees for data visualization.

Scientific Applications:

• Cytometry Data Visualization: FlowSOM offers visualization options for cytometry data.

Methodology:

FlowSOM uses Self-Organizing Map clustering and Minimal Spanning Trees.